Big Data involves technologies and procedures via which large data sets can be captured, can be stored and managed and later can be analysed (Srinivasan, 2017). The Big data helps in solving complicated problem skills.
The report will highlight the significant usage of Big Data in the various industries, especially highlight the Big Data usage in the financial firms and banks. Deutsche Bank’s Big Data adoption and their risks management will be showcased in the report.
Big Data analysis tools and statistical data
Big Data Technology consists of following aspects of the data analytics, the data storage, the data processing, and the data analytics (Srinivasan, 2017). The five major big data revenue generators are Oracle, Teradata, Hewlett-Packard, Dell and IBM. The four major aspects on which the Big Data technology depends are elaborately discussed as-
Infrastructure: The big data infrastructure is responsible to offer flexibility as well as scalability to manage the petabytes of data. The organizations if want to adopt the cloud technology then they must choose Rackspace, Amazon Web Services and GoGrid.
Data Analytics: The data analytics provide the visualization analysis of big data usage (Flood, 2016). The organisations can predict the outcomes of big data usage with the help of data analytics. Splunk, Progress dataDirect and Rainstor are the major providers of data analytics.
Data Storage: Hadoop is the widely used Big data storage platform. The major big data storage platform is MongoDB, Netezza and Clustrix.
Data Processing and Management: The Big data processing and management platform are used to process growing mass of data. Cloudera and Hadoop provide tools for data processing and management.
The industries and the organisations taking the benefits of Big data
In case of retail industry, many organisations have started to use the Big Data analytics to enhance their accuracy of the business forecasts, the analytics can help to anticipate the customers’ wishes and demands (Sharda et al., 2013). Thus, the big data analytics help the retailers to act accordingly based on the customers’ demands and changes (Srinivasan, 2017). Brooks Brother is one such organization for are using Big Data for their business.
In case of the transportation industry, the Frankfurt airport based in Germany utilizes the technology of SAS and because of Big Data technology, traffic controllers can be aware of the storms coming nearby (Qiu et al., 2016). The managers can know what they should do. They take the steps based on the report made via big data analysis like delays and the average time for the delivery of the luggage. The airport data gets refreshed after every five minutes and thus the managers and the staff of the airport can stay updated all the time. Because of the big data, the managers can access the data on the go. The Governments of USA also adopts the big data to facilitate their existing IT systems. The big data can help them to detect frauds, it can help them to generate new job opportunities (Srinivasan, 2017). The big data is helpful for them as they are using in military activities.
Big Data adoption and its impact on the financial industry
- The financial organisations consist of vast market data sets. The market dataset contains the historical information of the organization business deals and financial information, the big data helps to provide a predictive model comparing the historical information with the latest trends.
- The compliance requirements, as well as the financial requirements, are facilitating the risk reporting hence giving a better overview of financial market analysis all across global organisations (Haryadi et al., 2016).
- Financial organisations are developing risk management framework with the aid of big data (Srinivasan, 2017). The big data management methodologies assist in enhancing the executive oversight of risk, transparency as well as audibility.
- The big data helps the financial organisations to manage the vast amount of customer data across varied service channels like mobile. In this way, the financial organisations can know the customers’ demands, the customers’ wish lists (Hanson, Samuel & Jeremy, 2017). Thus, the customers can predict the overall market scenario with the help of big data. The big data helps in analysing the shopping behaviour of the customers.
- The markets emerging in the Asian subcontinents are gradually taking over the markets of US and Europe as huge investments are made in local as well as the cloud-based infrastructure of data (Haryadi et al., 2016).
- The big data management is responsible to reduce the cost of the business operations of the financial firms. The big data storage and associated framework help in gaining competitive advantage. It also gives them immense opportunities.
- The financial firms if replace their age-old ETL processes with big data frameworks then their data warehouse can handle a large piece of information (Jagadish et al., 2014).
- The predictive credit risk models help in analysing the vast amount of data that contains the customers' behaviour pattern, customers’ shopping lists and their payment pattern. In this way, the big data helps in prioritizing the collections activities by detecting the propensity for delinquency (Srinivasan, 2017).
- The mobile applications and the online activities have increased the demand for big data adaptation.
- Big data approaches give priorities to data security as well as data control, thus financial firms use big data for detecting frauds (Hanson, Samuel & Jeremy, 2017).
Case Study: Big Data and Risk Management in Deutsche Bank
Deutsche Bank has adopted the Big Data for conducting their business operations. The Bank has varied Hadoop platforms available via Open Source, this helps them in lessening the data processing expenditure. The Big Data also helps them to analyse the risks associated with the extraction of data from the bank’s systems that include the Volcker Key Performance Indicators, P&L Risk and Market Risk (Srinivasan, 2017). Deutsche Bank like other banks needs to keep Volker data for the longer duration of time, also they need to access those data quickly. The Big Data provides them with the opportunity to store their financial and trading data for over ten years. Besides, the Big Data also helps them to pull out those data in efficient and quick manner (Nyman et al., 2015). Deutsche Bank has also adopted another big data analytics method that is the matching algorithm, the matching algorithm helps them to get an overall view of the bank's performance. The profiling of data also helps them to recognize the abnormal data via rule-based algorithms.
It can be concluded from the above discourse that the Big Data can bring immense benefits to the financial organisations as well in the bank sectors. The report has highlighted the Big Data on other organisations and on other industries as well. The impact of Big Data on the financial firms have illustrated in the report quite well. The Big Data can assist the financial firms to mitigate the risks occurring within. Deutsche Bank has been chosen as the case study in this report. The Volcker Key Performance Indicators, P&L Risk and Market Risk have been showcased that can be mitigated with the effective implementation of Big Data.
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